Maximum Margin Projection Subspace Learning for Visual Data Analysis

Symeon Nikitidis, Anastasios Tefas, Ioannis Pitas

Research output: Contribution to journalArticle (Academic Journal)peer-review

27 Citations (Scopus)


Visual pattern recognition from images often involves dimensionality reduction as a key step to discover the latent image features and obtain a more manageable problem. Contrary to what is commonly practiced today in various recognition applications where dimensionality reduction and classification are independently treated, we propose a novel dimensionality reduction method appropriately combined with a classification algorithm. The proposed method called Maximum
Margin Projection Pursuit, aims to identify a low dimensional projection subspace, where samples form classes that are better discriminated i.e., are separated with maximum margin. The proposed method is an iterative alternate optimization algorithm that computes the maximum margin projections exploiting the
separating hyperplanes obtained from training a Support Vector Machine classifier in the identified low dimensional space. Experimental results on both artificial data, as well as, on popular databases for facial expression, face and object recognition verified the superiority of the proposed method against various
state-of-the-art dimensionality reduction algorithms.
Original languageEnglish
Pages (from-to)4413-4425
Number of pages13
JournalIEEE Transactions on Image Processing
Issue number10
Publication statusPublished - 18 Aug 2014


  • Maximum margin projections
  • support vector machines
  • face recognition
  • facial expression recognition
  • object recognition


Dive into the research topics of 'Maximum Margin Projection Subspace Learning for Visual Data Analysis'. Together they form a unique fingerprint.

Cite this